Modeling topics using statistical distributions

ABSTRACT

In one embodiment, modeling topics includes accessing a corpus comprising documents that include words. Words of a document are selected as keywords of the document. The documents are clustered according to the keywords to yield clusters, where each cluster corresponds to a topic. A statistical distribution is generated for a cluster from words of the documents of the cluster. A topic is modeled using the statistical distribution generated for the cluster corresponding to the topic.

RELATED APPLICATION

This application claims benefit under 35 U.S.C. §119(e) of U.S. Provisional Application Ser. No. 60/977,855, titled “Techniques for Topic Modeling,” filed Oct. 5, 2007, by David Marvit et al.

TECHNICAL FIELD

The present invention relates generally to lexicographical analysis and, more particularly, to modeling topics using statistical distributions.

BACKGROUND

A corpus of data may hold a large amount of information, yet finding relevant information may be difficult. Documents may be tagged to facilitate the search for relevant information. In certain situations, however, known techniques for document tagging are not effective in locating information.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates one embodiment of a system that models topics using statistical distributions that describe the topics;

FIG. 2 illustrates one embodiment of an affinity module that may be used with the system of FIG. 1;

FIG. 3 illustrates an example of an affinity matrix that records basic affinities;

FIG. 4 illustrates an example of an affinity matrix that records directional affinities;

FIG. 5 illustrates an example of an affinity matrix that records average affinities;

FIG. 6 illustrates an example of an affinity graph;

FIG. 7 illustrates one embodiment of a clustering module that may be used with the system of FIG. 1;

FIG. 8 illustrates one embodiment of an ontology feature module that may be used with the system of FIG. 1;

FIG. 9 illustrates one embodiment of a tagging module that may be used with the system of FIG. 1;

FIG. 10 illustrates an example of a method for determining a statistical distribution for a topic;

FIG. 11 illustrates an example of a method for assigning tags to a document by analyzing paragraphs of the document; and

FIG. 12 illustrates an example of a method for assigning tags in response to selected tags.

DETAILED DESCRIPTION Overview

In one embodiment, modeling topics includes accessing a corpus comprising documents that include words. Words of a document are selected as keywords of the document. The documents are clustered according to the keywords to yield clusters, where each cluster corresponds to a topic. A statistical distribution is generated for a cluster from words of the documents of the cluster. A topic is modeled using the statistical distribution generated for the cluster corresponding to the topic.

Example Embodiments

In particular embodiments, creating and querying a domain ontology may include the following:

1. Collect documents in a domain. In particular embodiments, a document is a collection of terms. A document may comprise readable text, for example, a book of the New Testament. A document need not comprise text in narrative form, for example, a document may comprise a set of user-entered tags that individually and collectively describe the content of an image. A collection of documents may be referred to as a “domain corpus.”

2. Identify the terms of interest (“dictionary terms”) in the domain. Examples of terms include a word (such as “tree”), a phrase (such as “graph algorithm”), a named entity (such as “New York”), etc. A term (or concept) may have different forms. In certain cases, different words are used for the same concept, for example, “kidney stones” and “kidney calculi” refer to the same concept, “kidney stones.” In other cases, a word stem may have many inflected variants, for example, the word stem “tree” has inflected variants “tree” and “trees.” In particular embodiments, forms of the same term may be treated as mapped to the same term. Any suitable form of a dictionary term may appear in a document, but the particular dictionary term need not appear in any document.

Examples of methods for identifying dictionary terms include using a human-generated dictionary for a specific domain, for example, a medical dictionary. In particular embodiments, a list of dictionary terms may be automatically generated from a set of strings of text in a corpus. The strings may be indexed and sorted by frequency, and strings with frequency above a threshold may be selected. Other suitable statistical method may be used to determine terms. In particular embodiments, “word” may be interchangeable with “term” and “dictionary term.”

3. Calculate the number of co-occurrences of dictionary terms in a given co-occurrence context. Two terms co-occur if they each appear at least once within the same co-occurrence context. Examples of co-occurrence contexts include a document and a paragraph.

4. Create a directed weighted graph that comprises the domain ontology. The directed weighted graph includes dictionary terms as the nodes and affinities as the weights of the edges. “Directed weighted graph” may be used as the actual representation of the same information that can be represented by any suitable data structure, e.g., a matrix, a Binary Decision Diagram, or a collection of Binary Decision Diagrams.

5. Apply a procedure to query the directed weighted graph. Given one or more dictionary terms as input, the procedure outputs one or more dictionary terms related to the input dictionary terms. For example, the procedure may outputs a sorted list of one or more terms that have the highest differential directional affinity (described below) towards one or more input terms. In this case, the output includes terms that are more closely related to the input terms, in relation to the domain that the ontology addresses.

Any suitable definitions of affinity may be used. In particular embodiments, the following may be used:

1. Basic Affinity

-   -   a. The basic affinity (A) between terms A and B may be defined         as the ratio of the number of co-occurrence contexts that         include both terms A and B over the number of co-occurrence         contexts that include either of the terms A or B:         A(A,B)=|AB|/|A or B|     -   b. The basic affinity (A) between terms A and B may also be         defined as the ratio of the number of co-occurrence contexts         that include both terms A and B over the maximum of either the         number of co-occurrence contexts that include A or the number of         co-occurrence contexts that include B:         A(A,B)=|AB|/max(|A|,|B|)

2. Directional Affinity

The directional affinity (DAff) between terms A and B may be defined as the conditional probability of observing B, given that A was observed in a co-occurrence context: DAff(A,B)=|AB|/|A| That is, directional affinity may be the number of co-occurrence contexts that include both terms A and B, over the number of co-occurrence contexts that include term A. Generally, DAff(A,B) differs from DAff(B,A).

3. Differential Directional Affinity

The differential directional affinity (DiffDAff) between terms A and B may be defined as the directional affinity between terms A and B minus a factor that accounts for the common-ness of the term B in the corpus. The common-ness of the term B in the corpus may be a statistical value over the basic affinity or directional affinity values of the term B towards the other terms in the corpus. In particular embodiment, the common-ness of the term B in a corpus may be the average affinity (AA) of term B, which yields the following definition of differential directional affinity: DiffDAff(A,B)=DA(A,B)−AA(B) The average affinity (AA), or average directional affinity, of a term B may be defined as: AA(B)=AVERAGE_xDAff(x,B) That is, average affinity may be the average of the directional affinities of a term B over the other terms in the co-occurrence contexts.

FIG. 1 illustrates one embodiment of a system 10 that models topics using statistical distributions that describe the topics. In particular embodiments, system 10 uses the statistical distributions of the related words of the topics. In the embodiments, system 10 accesses a corpus comprising documents. Keywords are selected from the words of the document, and the documents are clustered according to the keywords. A statistical distribution is generated for each cluster from words of the documents of the cluster. The statistical distribution is used to model the topic corresponding to the cluster. In particular embodiments, system 10 may use the statistical distributions to identify topics for which selected words of a document have the highest probability of appearance. The identified topics may be selected as tags for the document.

In certain embodiments, directional affinity may be calculated on a specific inverted index II for a given subset of words and a dictionary D, where index II includes, for example, entries I(w_(i)) and I(w_(j)) for words w_(i) and w_(j). In general, an inverted index is an index data structure that stores mappings from a term to its locations, that is, the co-occurrence contexts in which a term appears. For each pair of words w_(i) and w_(j) in D, DA(i,j) may be defined as the values in the conjunction of entries I(w_(i)),I(w_(j)) in II divided by the number of values in I(w_(i)). In general, DA(i,j) is not necessarily equal to DA(j,i). The results may be stored in any suitable manner, for example, row-wise, where the D(1,i) are stored, then the D(2,j) are stored, and so on. For each row i, |I(w_(i))| may be stored, followed by the cardinalities of the conjunctions with the W_(j).

In certain embodiments, directional affinity may be calculated in three phases. In the embodiments, each dictionary term is assigned a unique integer identifier. The entries of an inverted index correspond to the integer identifiers. In Phase 0, the II entries corresponding to D are read. For parameters (s, o), only the element identifiers that are of the form ks+o are kept. The value ks+o defines a subset of the II entries to be examined. In this manner, directional affinities can be computed in parallel. As an example, the result from parameters s,o (1,0) is equivalent to the one obtained from the merging of the computations with parameters (3,0), (3,1), (3,2). This step allows calculation of DA tables for very large inverted indices.

In Phase 1, the conjunctions are calculated row-wise only for DA(i,j). In Phase 2, the calculated upper-triangular UT DA array is read. From that, the lower-triangular part is obtained as the transpose of UT. In certain embodiments, multiple DA arrays of the same dimension may be merged into a single array. A DA array on a large II can be calculated as the sum_(i=0 . . . (s−1)) DA with parameters (s, i). Additional information may be stored with the calculated conjunctions so that directional affinities can be computed. In certain cases, the cardinalities of the II entries may be stored.

In certain embodiments, the DA may be stored row-wise, so the calculation of the AA entries may proceed in parallel with the calculation of the DA entries. In particular, AA may be generated by summing up the rows of the DA as they are read from the disk and, at the end, normalized by the number of the dictionary entries.

In the illustrated embodiment, system 10 includes a client 20, a server 22, and a memory 24. Client 20 allows a user to communicate with server 22 to generate ontologies of a language. Client 20 may send user input to server 22, and may provide (for example, display or print) server output to user. Server system 24 manages applications for generating ontologies of a language. Memory 24 stores data used by server system 24.

In the illustrated embodiment, memory 24 stores pages 50 and a record 54. A page 50 (or document or co-occurrence context) may refer to a collection of words. Examples of a page 50 include one or more pages of a document, one or more documents, one or more books, one or more web pages, correspondence (for example, email or instant messages), and/or other collections of words. A page 50 may be identified by a page identifier. A page 50 may be electronically stored in one or more tangible computer-readable media. A page 50 may be associated with any suitable content, for example, text (such as characters, words, and/or numbers), images (such as graphics, photographs, or videos), audio (such as recordings or computer-generated sounds), and/or software programs. In certain embodiments, a set of pages 50 may belong to a corpus. A corpus may be associated with a particular subject matter, community, organization, or other entity.

Record 54 describes pages 50. In the embodiment, record 54 includes an index 58, an inverted index 62, ontologies 66, and clusters 67. Index 58 includes index lists, where an index list for a page 50 indicates the words of the page 50. Inverted index 62 includes inverted index lists, where an inverted index list for a word (or set of words) indicates the pages 50 that include the word (or set of words). In one example, list W_(i) includes page identifiers of pages 50 that include word w_(i). List W_(i) & W_(j) includes page identifiers of conjunction pages 50 that include both words w_(i) and w_(j). List W_(i)+W_(j) includes page identifiers of disjunction pages 50 that include either word w_(i) or w_(j). P(W_(i)) is the number of pages 50 of W_(i), that is, the number of pages 50 that include word w_(i).

In one embodiment, a list (such as an index list or an inverted index list) may be stored as a binary decision diagram (BDD). In one example, a binary decision diagram BDD(W_(i)) for set W_(i) represents the pages 50 that have word w_(i). The satisfying assignment count Satisf(BDD(W_(i))) of a BDD(W_(i)) yields the number P(W_(i)) of pages 50 that have word w_(i): P(W _(i))=Satisf(BDD(W _(i))) Accordingly, P(W _(i)&W _(j))=Satisf(BDD(W _(i)) AND BDD(W _(j))) P(W _(i) +W _(j))=Satisf(BDD(W _(i)) OR BDD(W _(j)))

Ontologies 66 represent the words of a language and the relationships among the words. In one embodiment, an ontology 66 represents the affinities between words. In the illustrated example, ontologies 66 include an affinity matrix and an affinity graph. Examples of affinity matrices are described with the reference to FIGS. 3 through 5. An example of an affinity graph is described with reference to FIG. 6. Clusters 67 record clusters of words that are related to each other. Clusters are described in more detail with reference to FIG. 7.

In the illustrated embodiment, server 22 includes an affinity module 30, a clustering module 31, an ontology feature module 32, and a tagging module 35. Affinity module 30 may calculate affinities for word pairs, record the affinities in an affinity matrix, and/or report the affinity matrix. Affinity module 30 may also generate an affinity graph. Affinity module 30 is described in more detail with reference to FIG. 2.

In particular embodiments, clustering module 31 may discover patterns in data sets by identifying clusters of related elements in the data sets. In particular embodiments, clustering module 31 may identify clusters of a set of words (for example, a language or a set of pages 50). In general, words of a cluster are highly related to each other, but not to words outside of the cluster. A cluster of words may designate a theme (or topic) of the set of words. In particular embodiments, clustering module 31 identifies clusters of related words according to the affinities among the words. In the embodiments, words of a cluster are highly affine to each other, but not to words outside of the cluster. Clustering module 31 is described in more detail with reference to FIG. 7.

In particular embodiments, ontology feature module 32 may determine one or more ontology features of a set of one or more words (for example, a particular word or document that include words), and may then apply the ontology features in any of a variety of situations. An ontology feature is a feature of a word set that may place the word set in ontology space of a language. Examples of ontology features include depth and specificity. In particular embodiments, depth may indicate the textual sophistication of a word set. A deeper word set may be more technical and specialized, while a shallower word set may be more common. In particular embodiments, the specificity of a word set is related to the number of themes of the word set. A more specific word set may have fewer themes, while a less specific word set may have more themes.

Ontology feature module 32 may apply the ontology features in any suitable situation. Examples of suitable situations include searching, sorting, or selecting documents according to an ontology feature; reporting the ontology features of a document; and determining the ontology features of documents of one or more users. Ontology feature module 32 is described in more detail with reference to FIG. 8.

In particular embodiments, tagging module 35 may select tags to tag documents. Tags may be selected in any suitable manners. In particular embodiments, tagging module 35 models topics as statistical distributions of related words of the topics. Tagging module 35 uses the statistical distributions to identify topics for which selected words of a document have the highest probability of appearance and selects tags for the document in accordance with the identified topics. In other embodiments, tagging module 35 identifies candidate tags of the paragraphs of a document. Tagging module 35 determines the relatedness of the candidate tags with other candidate tags of the document and selects tags for the document in accordance with the determination. In yet other embodiments, tagging module 35 recommends tags for a document. The tags may be recommended based on affinity (for example, directional and/or differential affinity) with target tags input or selected by a user or by a computer. Once final tags have been selected, tagger 314 may assign the selected tags to the document. Tagging module 35 is described in more detail with reference to FIG. 9.

A component of system 10 may include an interface, logic, memory, and/or other suitable element. An interface receives input, sends output, processes the input and/or output, and/or performs other suitable operation. An interface may comprise hardware and/or software.

Logic performs the operations of the component, for example, executes instructions to generate output from input. Logic may include hardware, software, and/or other logic. Logic may be encoded in one or more tangible media and may perform operations when executed by a computer. Certain logic, such as a processor, may manage the operation of a component. Examples of a processor include one or more computers, one or more microprocessors, one or more applications, and/or other logic.

A memory stores information. A memory may comprise one or more tangible, computer-readable, and/or computer-executable storage medium. Examples of memory include computer memory (for example, Random Access Memory (RAM) or Read Only Memory (ROM)), mass storage media (for example, a hard disk), removable storage media (for example, a Compact Disk (CD) or a Digital Video Disk (DVD)), database and/or network storage (for example, a server), and/or other computer-readable medium.

Modifications, additions, or omissions may be made to system 10 without departing from the scope of the invention. The components of system 10 may be integrated or separated. Moreover, the operations of system 10 may be performed by more, fewer, or other components. For example, the operations of generators 42 and 46 may be performed by one component, or the operations of affinity calculator 34 may be performed by more than one component. Additionally, operations of system 10 may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, “each” refers to each member of a set or each member of a subset of a set.

Modifications, additions, or omissions may be made to the examples of the matrices without departing from the scope of the invention. A matrix may include more, fewer, or other values. Additionally, the values of the matrix may be arranged in any suitable order.

FIG. 2 illustrates one embodiment of affinity module 30 that may be used with system 10 of FIG. 1. Affinity module 30 may calculate an affinity for a word pair, record the affinity in an affinity matrix, and/or report the affinity matrix. Affinity module 30 may also generate an affinity graph.

In the illustrated embodiment, affinity module 30 includes an affinity calculator 34, ontology generators 38, and a word recommender 48. Affinity calculator 34 calculates any suitable type of affinity for a word w_(i) or for a word pair comprising a first word w_(i) and a second word w_(j). Examples of affinities include a basic, directional, average, differential, and/or other affinity.

In one embodiment, word recommender 48 receives a seed word and identifies words that have an affinity with the seed word that is greater than a threshold affinity. The threshold affinity may have any suitable value, such as greater than or equal to 0.25, 0.5, 0.75, or 0.95. The threshold affinity may be pre-programmed or user-designated.

A basic affinity may be calculated from the amount (for example, the number) of pages 50 that include words w_(i) and/or w_(j). The conjunction page amount represents the amount of pages 50 that include both word w_(i) and word w_(j), and the disjunction page amount represents the amount of pages 50 that include either word w_(i) or word w_(j). The basic affinity may be given by the conjunction page amount divided by the disjunction page amount. In one example, a number of conjunction pages indicates the number of pages comprising word w_(i) and word w_(j), and a number of disjunction pages indicates the number of pages comprising either word w_(i) or word w_(j). The basic affinity may be given by the number of conjunction pages divided by the number of disjunction pages: Affinity(w _(i) ,w _(j))=P(W _(i)&W _(j))/P(W _(i) +W _(j))

FIG. 3 illustrates an example of an affinity matrix 110 that records basic affinities. In the illustrated example, affinity matrix 110 records the pairwise affinities of words w₁, . . . , w₅. According to affinity matrix 110, the affinity between words w₀ and w₁ is 0.003, between words w₀ and w₂ is 0.005, and so on.

Referring back to FIG. 1, an affinity group includes word pairs that have high affinities towards each another, and may be used to capture the relationship between words w₁ and w₂ with respect to page content. A high affinity may be designated as an affinity over an affinity group threshold. A threshold may be set at any suitable value, such as greater than or equal to 0.50, 0.60, 0.75, 0.90, or 0.95. A word may belong to more than one affinity group. In one embodiment, an affinity group may be represented as a BDD. The pointer for the BDD may be stored with each word of the group in inverted index 62.

A directional affinity may be used to measure the importance of word w_(i) with respect to word w_(j). Affinity calculator 34 calculates the directional affinity of word w_(i) given word w_(j) from the amount (for example, the number) of pages 50 that include words w_(i) and w_(j). A word w_(j) page amount represents the amount of pages 50 that include word w_(i). The directional affinity of word w_(i) given word w_(j) may be given by the conjunction page amount divided by word w_(j) page amount. For example, a number of word w_(j) pages indicates the number of pages 50 that include word w_(i). The directional affinity of word w_(i) given word w_(j) may be given by the number of conjunction pages 50 divided by number of word w_(i) pages 50: DAffinity(w _(i) ,w _(j))=P(W _(i)&W _(j))/P(W _(i))

DAffinity(w_(i), w_(j)) is not the same as DAffinity(w_(j), w_(i)). A high directional affinity DAffinity(w_(i), w_(j)) between words w_(i) and w_(j) indicates a higher probability that a page 50 includes word w_(i) given that the page 50 includes word w_(j). In one example, pages [1 2 3 4 5 6] include word w_(i), and pages [4 2] include word w_(j). The pages that include word w_(j) also include word w_(i), so from the viewpoint of word w_(j), word w_(i) is of high importance. Only in one-third the pages that include w_(i) also include word w_(j), so from the viewpoint of word w_(i), word w_(j) is of low importance.

FIG. 4 illustrates an example of an affinity matrix 120 that records the directional affinities for words w₀, . . . , w₅ In the example, words 124 are A words, and words 128 are B words. The rows of matrix 120 record the affinity of a B word given an A word, and the columns of affinity matrix 120 record the affinity of an A word given a B word.

Referring back to FIG. 1, the average affinity of a word w_(i) calculated with respect to the other words w_(j). In one embodiment, the average affinity may be the average of the affinities between word w_(i) and every other word w_(j). The average affinity of word w_(i) of N words may be given by:

${{AveAff}\left( w_{i} \right)} = {\frac{1}{N}{\sum\limits_{j = 1}^{N}{P\left( w_{i} \middle| w_{j} \right)}}}$

FIG. 5 illustrates an example of an affinity matrix 140 that records average affinities. Rows 142 record basic affinities for word 1 through word 50,000. Row 144 records the average affinities of word 1 through word 50,000.

Referring back to FIG. 1, the average affinity of a word may indicate the depth of the word. A word with a lower average affinity may be regarded as a deeper word, and a word with a higher average affinity may be regarded as a shallower word. Deeper words tend to be more technical, specific, and precise. A page 50 with a higher percentage of deeper words may be regarded as a deeper page, and a page 50 with a lower percentage of deeper words may be regarded as a shallower page. In one embodiment, a user may specify the depth of word and/or pages 50 to be retrieved.

The deeper words of a page 50 may form one or more clusters of highly related words. A cluster may represent a common idea, or theme. The number of themes of a page 50 may indicate the specificity of the page 50. A page 50 with fewer themes may be regarded as more specific, and a page 50 with more themes may be regarded as less specific.

The differential affinity for word w_(i) with respect to word w_(j) is the directional affinity between words w_(i) and w_(j) minus the average affinity of word w_(j) for all other words. Differential affinity may be expressed as: DiffAff(w _(i) ,w _(j))=DAffinity(w _(i) ,w _(j))−AveAff(w _(j))

Differential affinity removes the bias caused by the general tendency for word w_(j) to occur in pages 50. In particular circumstances, differential affinity may provide a more accurate indication of the probability that a page includes word w_(i) given that the page includes word w_(j).

Differential affinities may be used in a variety of applications. In one example, differential affinities among people's names may be used to study social networking. In another example, differential affinities among language elements may be used to study natural language processing. In another example, differential affinities among products may be used to study marketing.

Affinity calculator 34 may use any suitable technique to search inverted index lists to calculate affinities. For example, to identify pages that include both words w_(i), and w_(j), affinity calculator 34 may search list W_(i) of word w_(i) and list W_(j) of word w_(j) for common elements, that is, common page identifiers.

In particular embodiments, an ontology generator 38 generates an ontology 66 of a language, such as an affinity matrix or an affinity graph. An ontology may be generated from any suitable affinity, such as a basic, directional, average, differential, and/or other affinity. Ontologies 66 may be generated from words selected from a language in any suitable manner. For example, words from a commonly used portion of the language or words related to one or more particular subject matter areas may be selected.

In the illustrated embodiment, ontology generators 38 include an affinity matrix generator 42 and an affinity graph generator 46. Affinity matrix generator 42 generates an affinity matrix that records affinities between words. Affinity graph generator 46 generates an affinity graph that represents affinities between words. In an affinity graph, a node represents a word, and the weight of the directed edge between nodes represents the affinity between the words represented by the nodes. An affinity graph may have any suitable number of dimensions.

FIG. 6 illustrates an example of an affinity graph 150. Affinity graph 150 includes nodes 154 and links 158. A node 154 represents a word. In the example, node 154 a represents the word “binary.” The weight of the directed edge between nodes between nodes 154 represents the affinity between the words represented by nodes 154. For example, a greater weight represents a greater affinity. A link 158 between the nodes indicates that the affinity between the words represented by the nodes 154 is above an affinity threshold. The affinity threshold may have any suitable value, for example, greater than or equal to 0.25, 0.5, 0.75, or 095.

FIG. 7 illustrates one embodiment of clustering module 31 that may be used with system 10 of FIG. 1. In particular embodiments, clustering module 31 discovers patterns in data sets by identifying clusters of related elements in the data sets. In particular embodiments, clustering module 31 may identify clusters of a set of words (for example, a language or a set of pages 50). In general, words of a cluster are highly related to each other, but not to words outside of the cluster. A cluster of words may designate a theme (or topic) of the set of words.

In particular embodiments, clustering module 31 identifies clusters of related words according to the affinities among the words. In the embodiments, words of a cluster are highly affine to each other, but not to words outside of the cluster. In one embodiment, words may be regarded as highly affine if they are sufficiently affine. Words may be sufficiently affine if they satisfy one or more affinity criteria (such as thresholds), examples of which are provided below.

Any suitable affinity may be used to identify clusters. In particular embodiments, clustering module 31 uses directional affinity. The directional affinity of a word with respect to other words characterizes the word's co-occurrence. A cluster includes words with similar co-occurrence. In certain embodiments, clustering module 31 uses differential affinity. Differential affinity tends to removes bias caused by the general tendency of a word to occur in pages 50

In the illustrated embodiment, clustering module 31 includes a clustering engine 210 and a clustering analyzer 214. Clustering engine 210 identifies clusters of word according to affinity, and clustering analyzer 214 applies affinity clustering to analyze a variety of situations.

Clustering engine 210 may identify clusters of words according to affinity in any suitable manner. Three examples of methods for identifying clusters are presented: building a cluster from a set of words, sorting words into clusters, and comparing affinity vectors of words. In one embodiment, clustering engine 210 builds a cluster from a set of words. In one example, clustering engine 210 builds a cluster S from a set W of words {w_(i)} with affinities *Aff(w_(i), w_(j)). Affinity value *Aff(w_(i), w_(j)) represents any suitable type of affinity of word w_(i) with respect to word w_(j), such as directional affinity DAffinity(w_(i), w_(j)) or differential affinity DiffAff (w_(i), w_(j)). Certain examples of affinity values provided here may be regarded as normalized values. In the example, Aff_(for)(w_(i), w_(j)) represents forward affinity, and Aff_(back)(w_(j), w_(i)) represents backward affinity.

In the example, cluster S starts with a seed word w_(q). The current word w_(x) represents a word of cluster S that is being compared with a word from set W at the current iteration. Initially, current word w_(x) is set to seed word w_(q).

During an iteration, current word w_(x) is set to a word of cluster S. Words w_(i) of set W are sorted according to their forward affinity Aff_(for)(w_(i), w_(x)) with current word w_(x). Starting at the beginning of the sorted set W, candidate words w_(c) that meet affinity criteria are identified. The affinity criteria may comprise a forward affinity with the current word w_(x) criterion: Aff_(for)(w _(c) ,w _(x))>Th _(cf) and a backward affinity with the seed word w_(q) criterion: Aff_(back)(w _(q) ,w _(c))>Th _(cb) where Th_(cf) represents a forward threshold for a candidate word, and Th_(cb) represents a backward threshold for a candidate word. The first words of an ordered set of candidate words {w_(c)} are added to the cluster S, the number of added words given by the parameter Size_(c). Thresholds Th_(cf) and Th_(cb) may be floating point parameters with any suitable values ranging from a minimum value to a maximum value. In certain examples, suitable values of Th_(cf) and Th_(cb) may be determined from a rank-ordered list of actual affinities. For example, the 200^(th) value in the list may be used. Parameter Sizes may be an integer parameter with any suitable value. Examples of suitable values include a default value of 1, 2, 3, or 4. In particular embodiments, the parameters may be varied at certain iterations.

Any suitable number of iterations may be performed. In one example, the number of iterations may be designated prior to initiation of the method. In another example, the number may be calculated during the performance of the method. For example, the number may be calculated from the growth rate of the size of cluster S.

In another embodiment, clustering engine 210, identifies clusters by sorting words of a set of words into clusters. In one example, the words {w_(i)} of set W are sorted according to affinities *Aff(w_(i), w_(j)), such as differential or directional affinities. In another example, the words {w_(i)} are sorted according to an aggregation function, such as the sum, of affinities of word w_(i) to each member of a distinct set of words Q. Set W may be selected in any suitable manner. For example, set W may be the X words most relevant to a query, where X may have any suitable value, such as a value in the range from 10 to 100, 100 to 200, or 200 or greater.

In the example, the clusters are initially empty. A first word w_(i) from set W is placed in a cluster. At each iteration, a current word w_(x) is selected from set W. Current word w_(x) is placed into a cluster if *Aff(w_(x), w_(f)) satisfies an affinity criterion given by an affinity threshold Th, where w_(f) represents the first word placed in the cluster. Threshold Th may have any suitable value, for example, a value in the range of 0.1 to 0.5 for a minimum value of 0.0 and a maximum value of 1.0. If *Aff(w_(x), w_(f)) does not satisfy threshold Th, current word w_(x) is placed into an empty cluster. The iterations are repeated for each word of set W.

After processing the words of set W, small clusters may be eliminated. For example, clusters with less than Y words may be eliminated. Y may have any suitable value, such as a value in a range of 3 to 5, 5 to 10, 10 to 25, 25 to 50, or 50 or greater.

If the number of clusters is not within a satisfactory range, the process may be repeated with a different value of threshold Th that yields a stricter or looser criterion for placement in a cluster. The satisfactory range may be given by a cluster number minimum and a cluster number maximum having any suitable values. Examples of suitable values include values in the range of 1 to 5, 5 to 10, or 10 or greater for the minimum, and values in the range of 10 to 15, 15 to 20, or 20 or greater for the maximum. The value of threshold Th may be increased to increase the number of clusters, and may be decreased to decrease the number of clusters.

In another embodiment, clustering engine 210 identifies clusters by comparing affinity vectors of words. In certain embodiments, the rows and columns of affinity matrix can yield affinity vectors <w_(i), *Aff(w_(i), w_(l)), . . . , *Aff(w_(i), w_(j)), . . . , *Aff(w_(i), w_(n))>, which represents the affinity of word w_(i) with respect to words w_(j), j=1, . . . , n. Affinity value *Aff(w_(i), w_(j)) represents any suitable type of affinity of word w_(i) with respect to word w_(j), for example, directional affinity or differential affinity.

In particular embodiments, affinity vectors with similar affinity values may indicate a cluster. For descriptive purposes only, an affinity vector may be regarded as coordinates of the affinity of a word in affinity space. That is, each affinity value *Aff(w_(i), w_(j)) may be regarded as a coordinate for a particular dimension. Affinity vectors with similar affinity values indicate that the words with which the vectors are associated are close to each other in affinity space. That is, the vectors indicate that the words have similar affinity relationships with other words and thus may be suitable for membership in the same cluster.

Affinity vectors may be similar if one affinity vector is proximate to the other affinity vector as determined by a suitable distance function. The distance function may be defined over the affinity vectors as, for example, the standard Euclidian distance for vectors of the given size, or as the cosine of vectors of the given size. The distance function may be designated by clustering engine 210 or by a user.

In particular embodiments, clustering engine 210 applies a clustering algorithm to identify affinity vectors with values that are proximate to each other. Examples of clustering algorithms include direct, repeated bisection, agglomerative, biased agglomerative, and/or other suitable algorithms. In one example, clustering engine 210 may include clustering software, such as CLUTO.

Clustering analyzer 214 may use affinity clustering for analysis in any suitable application. In one embodiment, clustering analyzer 214 may use affinity clustering to categorize pages 50. A category may be associated with a cluster identifier or one or more members of a cluster. In one example, clusters of a page 50 may identified, and then the page 50 may be categorized according to the clusters. In another example, important words of a page 50 may be selected, and then clusters that include the words may be located. The page 50 may then be categorized according to the located clusters.

In one embodiment, clustering analyzer 214 may use affinity clustering to analyze corpuses of pages 50. A corpus may be associated with a particular subject matter, community of one or more individuals, organization, or other entity. In one example, clustering analyzer 214 may identify clusters of a corpus and determine a corpus character of the corpus from the clusters. The corpus character may indicate the words relevant to the entity associated with the corpus. If one or more pages 50 have clusters of the corpus character, the pages 50 may be relevant to the entity.

In one embodiment, clustering analyzer 214 may use affinity clustering for search query disambiguation and expansion. In the embodiment, clustering analyzer 214 identifies clusters that include the search terms of a given search query. The clusters provide alternate words and/or categories relevant to the given search query. In one example, words from a cluster may be reported to a searcher to help with the next search query. In another example, clustering analyzer 214 may select words from the clusters and automatically form one or more new search queries. Clustering analyzer 214 may run the new queries in serial or parallel.

In one embodiment, clustering analyzer 214 may use affinity clustering to study a social network. In one example, pages 50 may provide insight into a social network. Examples of such pages include correspondence (such as letters, emails, and instant messages), memos, articles, and meeting minutes. These pages 50 may include words comprising user identifiers (such as names) of people of a social network. Clusters of names may be identified to analyze relationships among the people of the network. In one example, differential affinity clustering may be used to filter out names that appear most pages 50 without providing information, such as names of system administrators.

In particular embodiments, clustering analyzer 214 may analyze data sets by combining and/or comparing the clusters of the data sets. In one embodiment, clusters of overlapping data sets are compared. Clusters from one data set may be mapped to clusters of the other data set, which may provide insight into the relationships between the data sets. For example, the data sets may be from an analysis of documents of a group of colleagues and from a social networking study of the group. A social network cluster may be mapped to a document subject matter cluster to analyze a relationship between the social network and the subject matter.

FIG. 8 illustrates one embodiment of an ontology feature module 32. Ontology feature module 32 may determine one or more ontology features of a set of one or more words (for example, a particular word or document that include words), and may then apply the ontology features in any of a variety of situations. The set of one or more words may include essential terms of a document. A term t may be an essential term if at least one of the top k terms affined to term t is also present in the document. Otherwise, the term may be non-essential to the document.

An ontology feature is a quantifiable measure that characterizes a document along one or more axes of features that may distinguish the document, in a semantic sense, from other documents in a given area. For example, the depth of a document may distinguish the document with respect to its understandability, the specificity of a document may distinguish the document with respect to its focus, and the themes of a document may distinguish the document with respect to its addressed range of topics. An ontology feature can be defined in any suitable manner. For example, independent algorithms in computational linguistics may be used to characterize the readability, or depth, of the document.

In the illustrated embodiment, ontology feature module 32 includes a depth engine 230, a theme engine 240, a specificity engine 244, and an ontology feature (OF) application engine 250. Depth engine 230 may determine the depth of one or more words, for example, a particular word or document that include words. In general, depth may indicate the textual sophistication of words. Deeper words may be more technical and specialized, while shallower words may be more common. In particular embodiments, depth module 32 may calculate the depths of words of a document and then calculate the depth of the document according to the depths of the words. In particular embodiments, depth engine 230 may assign depth values and/or depth rankings to documents and/or words. A deeper document or word may be assigned a higher depth value or ranking, and a shallower document or word may be assigned a lower depth value or ranking.

Depth engine 230 may calculate word depth in any suitable manner. In particular embodiments, depth engine 230 calculates word depth from average affinities. In the embodiments, the depth of a word is a function of the average affinity of the word. A deeper word may have a lower average affinity, while a shallower word may have a higher average affinity. In particular examples, depth engine 230 may calculate the depths of words by ranking the words according to their average affinities. A word with a lower average affinity may be given a higher depth ranking, and a word with a higher average affinity may be given a lower depth ranking.

In particular embodiments, depth engine 230 may calculate word depth using a clustering analysis. In the embodiments, words of a cluster are highly affined to each other, but less so to words outside of the cluster. Distance in cluster space may be measured according to affinity, which may be an indicator of depth. In particular embodiments, words that belong to fewer clusters or to clusters that are smaller and/or farther away from other clusters may be regarded as deeper, and words that belong to more clusters or to clusters that are larger and/or closer to other clusters may be regarded as shallower.

In other particular embodiments, depth engine 230 may calculate word depth by applying a link analysis to an affinity graph 150. The link analysis may be performed by any suitable link analysis algorithm, for example, PAGERANK. For descriptive purposes only, affinity graph 150 of FIG. 6 may be used to calculate word depth. Affinity graph 150 includes nodes 154 and links 158. A node 154 represents a word. A link 158 between nodes 154 indicates that the affinity between the words represented by nodes 154 is above an affinity threshold, that is, the words are satisfactorily affined.

In particular embodiments, depth engine 230 calculates the popularity of nodes 154. A more popular node 154 may represent a shallower word, while a less popular node 154 may represent a deeper word. A link 136 from a first node 154 to a second node 154 is regarded as a popularity vote for the second node 154 by the first node 154. In addition, a vote from a more popular node 154 may have greater weight than a vote from a less popular node 154. Moreover, the affinity of a first node 154 to a second node 154 weights the vote. Depth engine 230 calculates the popularity of nodes 154 from the weighted votes for nodes 154. A less popular word may be regarded as deeper, and a more popular word with may be regarded as shallower.

Depth engine 230 may calculate document depth in any suitable manner. In particular embodiments, depth engine 230 calculates the depth of a document according to the depths of at least one, some, or all words of the document. In certain embodiments, word depth is given by average affinity, so the document depth may be calculated from average affinity of the words of the document. For example, the shallowness of a document may be the average of the average affinity of the words of the document, that is, the sum of the average affinity of each word in document divided by the total number of words in the document. The depth of the document may then be calculated as the inverse of the shallowness of the document.

In particular embodiments, depth may be calculated from the average depth of a selected set of words of the document. The selected set may include the essential words of the document, such as the top (deepest) X % words, where X may be less than 10, 10 to 20, 20 to 30, 30 to 40, 40 to 50, 50 to 60, 60 to 70, or greater than 10. The selected set may exclude P % of the standard grammar words and/or Q % of the stop words, where P and Q have any suitable values, such as less than 10, 10 to 20, 20 to 30, 30 to 40, 40 to 50, 50 to 60, 60 to 70, or greater than 10

In particular embodiments, depth engine 230 calculates the depth of a document according to the distribution of word depth in the document. In particular embodiments, a deeper document may have a higher percentage of deeper words.

In particular embodiments, depth engine 230 calculates the depth of a document according to document affinity. The affinity between documents describe the relationship between the documents. In certain embodiments, the average document affinity may indicate document depth in a manner similar to how the average word affinity may indicate word depth. Document affinity may be defined in any suitable manner. In one example, the number of common words P(D₁ & D₂) indicates the number of words in both documents D₁ and D₂, and the number of distinct words P(D₁+D₂) indicates the number of words in either document D₁ or D₂. Document affinity DocAff between documents D₁ and D₂ may be defined as: DocAff(D ₁ ,D ₂)=P(D ₁&D ₂)/P(D ₁ +D ₂) Depth engine 230 may calculate an average document affinity that in a manner similar to the calculation of average word affinity. A document with a lower average affinity may be regarded as deeper, and a document with a higher average affinity may be regarded as shallower.

In certain embodiments, depth engine 230 may calculate document depth by applying a link analysis to a document affinity graph. A document affinity graph may be similar to affinity graph 150, except that nodes of a document affinity graph represent documents instead of words. Depth engine 230 weights a link from a first node representing a first document to a second node representing a second document with the document affinity of the second document given the first document. The weights of the outgoing links may then be normalized.

In certain embodiments, a depth graph may be displayed on a user interface to show the depths of documents. A depth slider that can be used to select a depth level may also be displayed. In certain embodiments, if a document comprises sections of a larger document, the depth graph can indicate the depths of the sections.

In certain embodiments, depth engine 230 may calculate document depth in any other suitable manner, such as processing histograms of affinities of a document and/or truncating percentages of distinct words based upon depth and then processing the histograms. Other methods include the Gunning-Fog, Flesch, or Fry methods.

In certain embodiments, depth engine 230 may calibrate depth by mapping depth values to particular depth levels. In certain embodiments, depth values in range R_(i) may be mapped to level L_(i). For example, R₀={r₀: r₀<c₀} may be mapped to level L₀, R₁={r₁: c₀<r₁<c₁} to level L₁, . . . , and R_(n)={r_(n): c_(n)<r_(n)} to level L_(n). The ranges may include any suitable depth values and need not be of the same size. There may be any suitable number of levels, such as less than five, five to seven, seven or eight, eight to ten, ten to 20, 20 to 50, 50 to 100, or greater than 100.

Theme engine 240 may determine the themes (or topics) of a document. In particular embodiments, theme engine 240 determines the themes from the clusters of words in the document, which may be identified by clustering module 31. As discussed above, a cluster of words may designate a theme (or topic) of the set of words. The theme of a document may provide useful information about the content of the document. For example, a document that includes the cluster {renal, kidney, protein, problem} is probably about protein leaking from the kidney due to weakening renal functions, rather than the protein content of kidney beans.

In particular embodiments, theme engine 240 determines themes from a theme map. In the embodiments, keywords are extracted from the document using any suitable technique, for example, a term frequency-inverse document frequency (TF-IDF) technique. The keywords are used to select candidate themes from the theme map. The candidate themes are compared to the document to determine how well the themes match the document. In certain examples, a histogram of the candidate themes may be compared to a histogram of the document. If the candidate themes match the document, the themes can provide an estimate of the types and number of themes of the document.

Specificity engine 240 may calculate the specificity of a document. In particular embodiments, specificity engine 240 may assign specificity values and/or specificity rankings to documents. A more specific document may be assigned a higher specificity value or ranking, and a less specific document may be assigned a lower specificity value or ranking.

In particular embodiments, specificity engine 240 calculates the specificity from the number of themes of the document. In certain examples, a more specific document may have fewer themes, and a less specific document may have more themes. In particular embodiments, specificity engine 240 calculates the specificity from the number of themes of the document and the affinity between the themes. In certain examples, a more specific document may have fewer themes with higher affinity between the themes, and a less specific document may have more themes with lower affinity between the themes.

In particular embodiments, the number of themes may be dependent on depth (or level). For example, a single theme at a shallower depth might represent multiple themes at a greater depth. In certain embodiments, the depth may be selected by a user using a depth slider or may be predetermined. In certain embodiments, the level may be selected by a user or may be predetermined. For example, any suitable number of levels may be defined, and the depth may be calculated with respect to the level. For example, the levels may be domain based (for example, engineering, medical, news, sports, or finance domain); specialty based (for example, cardiology, ophthalmology, or nephrology specialty); topic based (for example, hypertension, cholesterol, bypass surgery, or artery-blocks topic); details based (for example, postural hypotension, chronic hypertension, or acute hypertension detail); resolution based (for example, geriatric etiology, medicinal, or genetic resolution); person based (for example, the user query level).

Ontology feature application engine 250 may apply ontology features (such as depth, themes, or specificity) to perform an ontology feature analysis in any suitable situation. Examples of suitable situations include: searching, sorting, recommending, or selecting documents according to an ontology feature; reporting the ontology features of a document; and determining the ontology features of documents (or sets of documents) of one or more users. In particular embodiments, ontology feature application engine 250 may use indices that include information about an ontology feature. In one example, ontology feature application engine 250 uses a document depth (DD) inverted index 62 that is generated and/or maintained according to depth ranking. DD inverted index 62 includes DD inverted index lists, where a DD inverted index list for a word lists document identifiers of documents (or pages 50) that include the word. The document identifier of a document may indicate the depth of the document. For example, the binary encoding used to encode the document identifiers may indicate the depth. In some cases, the DD inverted index lists may list only documents of a satisfactory depth. In another example, ontology feature application engine 250 uses a ranking table and a depth table in addition to inverted index 62. The depth table may indicate the depths of the documents.

In particular embodiments, ontology feature application engine 250 searches for documents with specified values of an ontology feature, such as specified values of document depth or specificity. The specified values may be predetermined, calculated, or selected by a user. In particular embodiments, the values may be selected using a depth slider and/or a specificity slider.

In particular embodiments, ontology feature application engine 250 may use an ontology feature as a sort criterion to sort documents. For example, ontology feature application engine 250 may sort documents according to document depth and/or specificity with respect to themes as well as other sort criteria. In certain examples, ontology feature application engine 250 searches DD inverted index 62 to obtain documents sorted according to document depth. In some examples, ontology feature application engine 250 searches for documents using a non-DD inverted index 62 and then sorts the documents according to depth.

In particular embodiments, ontology feature application engine 250 may graphically display the values of an ontology feature to a client 20. The graphical displays may be provided for some or all documents, for example, for the documents from the top X % of search results. The ontology feature values may be presented in any suitable manner. In some examples, a graphical indicator, such as a number, word, or icon, may indicate a value. The graphical indicator may be placed next to, for example, an item in a list of search results, a headline of an online newspaper, or a document icon. In some examples, modification of existing iconography may indicate the value. For example the size, font, style, color, of text or a graphical indicator may indicate a value. In another example, a graph may indicate the values. An ontology feature histogram may include a document amount axis and a ontology feature axis, and may indicate the amount of documents of particular ontology feature values. For example, a document depth histogram that includes a document amount axis and a document depth axis may indicate the amount of documents of particular document depths.

In particular embodiments, ontology feature application engine 250 may allow a user to request a search for documents that have particular ontology feature values. The user may be allowed to specify values for different words of a query. In certain examples, ontology feature application engine 250 may provide a user with the option to select a depth, and the user may then input the selected depth. The options may be presented in any suitable manner, such as in: (i) absolute terms (for example, a number or a range of numbers representing depth); (ii) relative terms (for example, a portion of search results with respect to depth, such as “deepest X %”); (iii) semantic terms (for example, ‘introductory’, ‘shallow’, ‘deep’, ‘very deep’, and/or ‘highly technical’); (iv) graphical terms (for example, a slider, a button, and/or other graphical element); or (v) any suitable combination of terms (for example, a slider with semantic labels). In some cases, a slider may include a shallow end and a deep end. A user may move the slider toward one end or the other to indicate a selected depth. When the search results are provided, a document depth histogram may appear by the slider, and may use the slider as the document depth axis.

In particular embodiments, ontology feature application engine 250 may calculate an ontology feature character of a set of one or more users. Ontology feature characters may include user depth and user specificity in the context of a theme. The ontology feature character describes the ontology features of documents associated with the user set. For example, a scientist may use deeper documents than a third grader would use. The ontology feature character may be given with respect to one or more themes. For example, a geneticist may use deeper documents in the field of genetics than he would use in the field of poetry. The ontology feature character may be used to determine the expertise of a user, automatically build a resume for a user, and analyze the social network of a user.

Any suitable documents associated with a user may be analyzed to estimate the ontology feature character, for example, correspondence (such as email and instant messages), web pages, and search history (such as search queries and selected pages). In particular embodiments, ontology feature application engine 250 may track an ontology feature character over time, and may use the past character to predict a future character. In certain examples, ontology feature application engine 250 may assume that a user depth and/or specificity generally increases with time and/or activity in an area.

In particular embodiments, ontology feature application engine 250 may combine certain operations. For example, ontology feature application engine 250 may monitor the depth of a user and then search for documents according to the user depth. In one example, user depth is monitored, and news is provided to the user according to the depth. Future user depth is predicted, and news that fits the predicted user depth is provided.

FIG. 9 illustrates one embodiment of a tagging module 35 that may select tags to tag documents. Tags may be selected in any suitable manner. In particular embodiments, tagging module 35 models a topic (or theme) as the statistical distribution of related words of the topic. Tagging module 35 uses the statistical distributions to identify topics for which high ranking words of a document have the highest probability of appearance and selects tags for the document in accordance with the identified topics. In the illustrated embodiment, tagging module 35 includes a topic modeler 310 and a document tagger 314. In particular embodiments, topic modeler 310 generates statistical distributions that model topics, and document tagger 314 selects tags based on the statistical distributions. Topic modeler 310 and document tagger 314 may any suitable method to model topics and select tags. An example of a method is described with reference to FIG. 10.

In other embodiments, tagging module 35 assigns tags by analyzing paragraphs of the document. In the embodiments, tagging module 35 identifies candidate tags of the paragraphs of a document. Tagging module 35 determines the relatedness of the candidate tags with other candidate tags of the document and selects tags for the document in accordance with the relatedness. An example of a method for assigning tags by analyzing paragraphs of the document is described in more detail with reference to FIG. 11.

In yet other embodiments, tagging module 35 may assign tags based on recommended tags that were selected by a user or by a computer. In the embodiments, tagging module 35 recommends tags for a document. The recommended terms may have a higher affinity with a target tag, but a lower affinity with respect to each other to reduce the ontology space of the document. Tagging module 35 may continue recommending tags in response to the selected tags. Once final tags have been selected, tagging module 35 may assign the selected tags to the document. An example of a method for assigning tags is described in more detail with reference to FIG. 12.

FIG. 10 illustrates an example of a method for assigning tags according to the statistical distributions of topics. The statistical distributions may be generated from a universe of words. Any suitable universe may be used, such as the words of a language or a corpus (for example, the Internet). Words appropriate to a topic may have a higher relative probability of appearance than that of other words. For example, for the topic “bicycle,” words such as “tire,” “chain,” and “riding” may have a higher relative probability than that of words such as “brick,” “bucket,” and “pizza.”

The method starts at step 410, where terms of the documents of a corpus are ranked using any suitable ranking technique. In one example of a ranking technique, terms are ranked according to frequency (for example, term frequency or term frequency-inverse document frequency (TF-IDF)). A higher frequency may yield a higher ranking. In another example of a ranking technique, terms are ranked according to the number of standard deviations a term's co-occurrence with other terms is above random chance. A higher number of standard deviations may yield a higher ranking.

One or more highly ranked terms are selected as the keywords of the documents at step 414. In some examples, the top ranked N terms may be used, where N may be one to five, five to ten, or ten or more. In other examples, the terms that are a predetermined distance (such as one standard deviation) above the average ranking for the document may be used.

The documents are clustered according to their keywords at step 418, where the clusters are associated with the keywords. The keyword defined for a cluster is the topic of the cluster. If a document has N keywords, then the document will be represented in N clusters. Small clusters are removed at step 422. A small cluster may be a cluster that fails to satisfy a size threshold, for example, a cluster that represents less than M documents, where M may be a value in the ranges 0 to 50, 50 to 100, or 200 or greater. In some examples, M may be calculated from the size of the corpus. For examples, M may be a value in the ranges 0% to 3%, 3% to 5%, or 5% or greater.

Statistics of a cluster are collected at step 426, and a statistical distribution for the cluster is generated from the statistics at step 428. Any suitable statistics may be collected to generate any suitable statistical distribution (such as a frequency and/or probability distribution). In certain examples, a term frequency indicating the frequency of a word in a cluster is calculated for each word of the cluster. The term frequency may be calculated from the number of appearances of the word in the cluster or from the number of documents in the cluster that include the word. A term distribution is generated from the term frequencies. In other examples, a co-occurrence value indicating the co-occurrence of the topic of the cluster with the topics of another cluster is calculated for each of the other clusters. A co-occurrence distribution is generated from the co-occurrence values. If there is a next cluster at step 430, the method returns to step 426 to collect the statistics of the next cluster. If there is no next cluster at step 430, the method proceeds to step 434.

Clusters with similar statistical distributions are consolidated at step 434. Statistical distributions may be compared, and similar statistical distributions may be consolidated into a single frequency distribution. For example, the clusters for the topics “car” and “automobile” may have similar statistical distributions, so they are consolidated into a single cluster. Statistical distributions may be regarded as similar if the differences between the distributions is less than a difference threshold. The difference threshold may have any suitable value, such as a value in the range of less than or equal to 1%, 5% to 10%, or 10% or greater. The topic of the larger cluster may be selected as the topic for the consolidated cluster.

Topics are reassigned to as tags the documents based on the resulting clusters at step 438. Since some clusters have been consolidated and other clusters have been removed, the topics assigned to the documents may have changed. The reassigned topics may serve as more informative, less duplicative tags for the documents. The method then ends. The method may be performed as the documents of the corpus are updated.

Tags are assigned to documents at step 442. Document tagger 314 may assigns tag to documents in accordance with statistical distributions in any suitable manner. In some examples, document tagger 314 may assign tags to the documents in the corpus in accordance with the reassignment of topics performed at step 438.

In other examples, document tagger 314 may assign tags to documents that are not necessarily in the corpus. The statistical distributions may be used to identify topics for which selected words of the document have a high probability of appearance, and the identified topics may be selected as tags. In the examples, document tagger 314 ranks the words of the document according to any suitable ranking technique, such as discussed above. Starting with the highest ranking word, document tagger 314 determines a frequency of the word for each topic from the statistical distributions of the topics. Document tagger 314 may then rank the topics from the topic for which the word is most frequent to the topic for which the word is least frequent. A statistical distribution of the word with respect to the topics may be generated.

In the examples, document tagger 314 may then generate statistical distributions in a similar manner for one or more other highly ranked words of the document. In certain examples, the statistical distributions of the words may be weighted, for example, equally or according to the ranking of the words. For example, a higher ranking word may have a statistical distribution with a greater weight. The statistical distributions may be consolidated to yield a consolidated statistical distribution. In particular embodiments, the weighted statistical distributions may be summed. For example, values associated with a particular topic are summed to yield a value that indicates the likelihood of the topic given the highly ranked words of the document. Document tagger 314 may assign one or more likely topics as tags for the document.

FIG. 11 illustrates an example of a method for assigning tags to a document by analyzing paragraphs of the document. The method may be used for documents that includes micro-ideas, ideas, and hypotheses. In particular embodiments, a micro-idea comprises an independent, self-contained, unit of expression. One or more related micro-ideas may form an idea. One or more related ideas may form a hypothesis. In certain examples, a sentence expresses a micro-idea, a paragraph expresses an idea, and a series of related paragraphs expresses a hypothesis. In the examples, the paragraphs are related, so the core terms of the paragraphs may have a relatively high directional affinity. Some intersection of the core terms may be used as a tag.

The method starts at step 506, where paragraphs P_(i) of a document are identified as text units for analysis. A paragraph may refer to any suitable set of characters, words, and/or sentences designated in any suitable manner, for example, by a fixed or variable number of words, by a paragraph mark, or by clustering. A paragraph may be defined to include, for example, a sufficient number of words of sufficient complexity.

A paragraph P_(i) is selected at step 510. A candidate tag set S_(i)=<t₁, t₂, . . . , t_(m)> of tags t_(k) is established for paragraph P_(i) at step 514. In particular embodiments, higher ranking words may be selected as candidate tags. (The words may be ranked according to any suitable ranking technique.) In particular embodiments, the initial number of candidate tags may be selected in accordance with the desired resulting number of candidate tags. For example, if the desired resulting number is k, then the initial number may be c*k, where c>l. Parameter c may have any suitable value, for example, c=2, 3, 4, or 5. A highest ranking candidate tag may be selected as the root r_(i) for set S_(i).

Relatedness of the candidate tags to each other is determined according to any suitable relatedness technique at step 518. In general, relatedness may be measured in any suitable manner, for example, using any suitable affinity. For example, a tag that is more affine with a target tag may be regarded more related, and a tag that is less affine with a target tag may be regarded less related. In particular embodiments, the tags may be clustered (using, for example, directional and/or differential affinities), and the tags of a cluster may be regarded as related.

Preference weights are assigned to candidate tags at step 520. Preference weights may be assigned according to any suitable ranking technique. For example, a greater preference weight may be given to a candidate tag with a higher frequency in the paragraph and/or a greater inverse of average affinity in the document. Candidate tags that are not sufficiently related to other candidate tags are removed from the candidate tag set at step 524. Any suitable relatedness threshold may designate whether a tag is sufficiently related to other tags. There may be a next paragraph at step 530. If there is a next paragraph, the method returns to step 510 to select the next paragraph. If there is no next paragraph, the method proceeds to step 534.

The relatedness of candidate tag sets of different paragraphs is determined at step 534. Relatedness may be determined according to any suitable relatedness technique. Similar to the case of intra-paragraph analysis, in particular embodiments, the candidate tags may be clustered, and the candidate tags of a cluster may be regarded as sufficiently related In other embodiments, a co-relation profile may be generated for each candidate tag. The co-relation profile indicates the co-relatedness of the candidate tag to other candidate tags, for example, the tags of other candidate tag sets. A greater co-relatedness represents greater relatedness.

A co-relation profile may be computed in any suitable manner. In some examples, a co-relation profile of a candidate tag is generated from the number of candidate tag sets that include the candidate tag, and may take into the account the frequency of the candidate tag in the candidate tag sets. A candidate tag that appears in more candidate tag sets with greater frequency may have greater co-relatedness.

In other examples, the co-relation profile of a candidate tag of set S_(i) (with root r_(i)) with respect to other sets S_(j) (with roots r_(j)) may be determined from the directional affinities of the roots r_(i) and r_(j). In these examples, the co-relatedness value of a candidate tag of set S_(i) and a particular set S_(j) may be calculated by multiplying the preference weight of the candidate tag with the directional affinity of roots r_(i)→r_(j) over set S_(j). The co-relatedness of the candidate tag sets S_(j) may be calculated by combining (for example, summing up) the co-relatedness values of the particular sets.

In yet other examples, the co-relation profile of a candidate tag t_(i) of set S_(i) with respect to other sets S_(j) (with tags t_(j)) may be determined from the directional affinities of the individual tags t_(i) and t_(j). In these examples, the co-relatedness value of a candidate tag of set S_(i) and a particular set S_(j) is calculated by determining directional affinity of tags t_(i)→t_(j) over set S_(j), and summing the directional affinities. The co-relatedness of the candidate tag and sets S_(j) may be calculated by combining the co-relatedness values of the particular sets.

Tags are selected from the candidate tags at step 538. In particular embodiments, candidate tags that are most highly related to the other candidate tags are selected. In some examples, the highest ranking candidate tags of the clusters may be selected. In other examples, candidate tags with the highest co-relatedness according to the co-relation profiles may be selected. The number k of selected tags may be a predetermined constant or may be a value determined from the depth of the query terms. For example, for queries with deeper terms, a smaller or larger k may be used. The method then ends.

FIG. 12 illustrates an example of a method for assigning tags in response to selected tags. The method starts in an initial phase at step 450. In the initial phase, document tagger 314 receives an initial tag as a target tag for a document. The initial tag may be from any suitable source. For example, the initial tag may be input by a user or by logic (such as a computer). The logic may input tags resulting from an analysis of the document, other documents associated with a user, or other tags selected for the document. Document tagger 314 may record the source of the tag.

In particular embodiments, document tagger 314 may initiate display of a graphical user interface at client 20 that allows for a user to interact with document tagger 314. In some examples, the interface may allow a user to request addition or deletion of a tag. In other examples, the interface may include a graphical element that allows a user to indicate a desired degree of relatedness that a tag should have to a particular term. For example, the interface may include a slider that may be moved closer to the term to indicate a higher degree or farther away from the term to indicate a lower degree.

In a candidate phase at step 454, document tagger 314 recommends terms in response to the input tag. The recommended terms may be selected to associate a document with a minimal amount of ontology space. For example, the recommended terms may have a higher affinity with the input tag, but a lower affinity with respect to each other. For example, if the input tag is “tree,” then the recommended tags may be “plant”, “family”, or “computer science.”

The recommended terms may avoid over specification and under specification. Over specification results from providing essentially ontologically redundant tags that do not provide much additional information. For example, if a document has the tags “tree” and “woods,” then adding “forest” does not provide much additional information. Under specification results from providing tags that do not disambiguate a document. For example, the tag “bank” of a document does not specify if the document deals with a financial institution, a river, or the edge of a billiard table.

In a testing phase at step 458, document tagger 314 monitors recommended terms that were selected (for example, by the user) and terms that were not selected, or rejected. For example, document tagger 314 receives “fluid” and recommends “adaptable,” “flexible,” “liquid,” “solution,” and “melted.” Document tagger 314 notes that “liquid” and “melted” were rejected, so document tagger 314 does not recommend “solution.” The selected terms are added to the set of target tags.

In particular embodiments, document tagger 314 may record the source of the tags, for example, a user or logic (such as a computer). The source may have any suitable application. For example, the source may be used to rank search results. In one example, search results that have tags selected by a user may be more highly ranked than results that have tags generated by logic.

In an evolution phase at step 462, document tagger 314 evaluates the differences between recommended terms and selected terms to recommend new terms. Document tagger 314 may recommend terms that have a higher affinity (for example, directional and/or differential affinity) with the selected terms and/or a lower affinity with the rejected terms, and may avoid recommending terms that have a higher affinity with the rejected terms and/or a lower affinity with the selected terms. In particular embodiments, document tagger 314 may remove one or more ontologically redundant tags. Tags may be recommended and selected for any suitable number of iterations, such as one to five, six to 10, or 10 or greater iterations.

In an assignment phase at step 466, document tagger 314 assigns one or more tags to the document. In particular embodiments, document tagger 314 may assign tags in response to the testing phase or to one or more initial tags, independent of the testing phase. The method then ends.

Modifications, additions, or omissions may be made to the methods without departing from the scope of the invention. The methods may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order.

In particular embodiments, the methods may be performed to select search terms instead of tags. The embodiments may be described by replacing “tag” with “search term” in the descriptions included in this document, in particular, the descriptions associated with the methods for assigning tags.

For example, a method may start in an initial phase. In the initial phase, an initial search term is received as a target search term for a search. The initial search term may be from any suitable source, for example, input by a user or by logic (such as a computer). In a candidate phase, terms may be recommended in response to the input search term. The recommended terms may be selected to associate the search with a minimal amount of ontology space. In a testing phase, recommended terms that were selected (for example, by the user) and terms that were not selected, or rejected, may be monitored. In an evolution phase, the differences between recommended terms and selected terms to recommend new terms may be evaluated. Search terms may be recommended and selected for any suitable number of iterations, such as one to five, six to 10, or 10 or greater iterations. Search terms may be selected in response to the selected search terms.

Modifications, additions, or omissions may be made to the methods without departing from the scope of the invention. The methods may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order.

Certain embodiments of the invention may provide one or more technical advantages. A technical advantage of one embodiment may be that the statistical distribution of the related words of a topic are used to model the topic. The statistical distributions may be used to selects tags for a document. For example, statistical distributions may be used to identify topics for which selected words of a document have the highest probability of appearance. The identified topics may be selected as tags for the document.

Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the spirit and scope of this disclosure, as defined by the following claims. 

What is claimed:
 1. A computer-implemented method comprising: accessing a corpus stored in one or more tangible media, the corpus comprising a plurality of documents, a document comprising a plurality of words; selecting one or more words of each document as one or more keywords of the each document; clustering the documents according to the keywords to yield a plurality of clusters, each cluster corresponding to a different topic; generating a statistical distribution for each cluster from a subset of the words of the documents of the each cluster to yield a plurality of statistical distributions, wherein generating the statistical distribution for the each cluster comprises: determining a co-occurrence value indicating a co-occurrence of the topic of the each cluster with the topics of the other clusters in the plurality of documents; and generating a co-occurrence distribution from the co-occurrence values; modeling each topic using the statistical distribution generated for the cluster corresponding to the each topic; organizing the clusters according to the statistical distributions; and assigning the topics of the organized clusters to the documents in the organized clusters.
 2. The method of claim 1, the selecting the one or more words of the each document further comprising: ranking the words of the each document according to a ranking technique; and selecting one or more highly ranked words as the one or more keywords.
 3. The method of claim 1, the clustering the documents according to the keywords to yield the plurality of clusters further comprising: removing one or more clusters that fail to satisfy a size threshold.
 4. The method of claim 1, the generating the statistical distribution for the each cluster further comprising: calculating a term frequency of each word of the subset of the words to yield a plurality of term frequencies; and generating a term distribution from the term frequencies.
 5. The method of claim 1, the generating the statistical distribution for the each cluster further comprising: calculating a number of documents that include each word of the subset of the words; and generating a term distribution from the numbers of documents.
 6. The method of claim 1, further comprising: identifying at least two clusters with similar statistical distributions; and consolidating the at least two clusters.
 7. One or more non-transitory computer-readable tangible media encoding software operable when executed to: access a corpus stored in one or more tangible media, the corpus comprising a plurality of documents, a document comprising a plurality of words; select one or more words of each document as one or more keywords of the each document; cluster the documents according to the keywords to yield a plurality of clusters, each cluster corresponding to a different topic; generate a statistical distribution for each cluster from a subset of the words of the documents of the each cluster to yield a plurality of statistical distributions, wherein generating the statistical distribution for the each cluster comprises: determining a co-occurrence value indicating a co-occurrence of the topic of the each cluster with the topics of the other clusters in the plurality of documents; and generating a co-occurrence distribution from the co-occurrence values; and model each topic using the statistical distribution generated for the cluster corresponding to the each topic; organize the clusters according to the statistical distributions; and assign the topics of the organized clusters to the documents in the organized clusters.
 8. The computer-readable tangible media of claim 7, further operable to select the one or more words of the each document by: ranking the words of the each document according to a ranking technique; and selecting one or more highly ranked words as the one or more keywords.
 9. The computer-readable tangible media of claim 7, further operable to cluster the documents according to the keywords to yield the plurality of clusters by: removing one or more clusters that fail to satisfy a size threshold.
 10. The computer-readable tangible media of claim 7, further operable to generate the statistical distribution for the each cluster by: calculating a term frequency of each word of the subset of the words to yield a plurality of term frequencies; and generating a term distribution from the term frequencies.
 11. The computer-readable tangible media of claim 7, further operable to generate the statistical distribution for the each cluster by: calculating a number of documents that include each word of the subset of the words; and generating a term distribution from the numbers of documents.
 12. The computer-readable tangible media of claim 7, further operable to: identify at least two clusters with similar statistical distributions; and consolidate the at least two clusters. 